Latent Class Analysis for Health-Related Quality of Life in Nurses in China

Abstract

Abstract Background This study aimed to identify the types of quality of life (QoL) based on the five dimensions of the EQ-5D and predict factors affecting QoL. Methods A multistage stratified cluster sampling survey was conducted among the staff of 12 general hospitals, 1965 nurses completed the survey, and the data were analysed using SPSS 26.0 and Mplus 8.3 for latent analysis. Results Three latent classes of QoL were identified: low-level (2.8%), pain and discomfort (7.6%), medium-level (47.1%), and high-level (42.5%). The types and characteristics of QoL differed among these latent classes. The low-level group had the lowest EQ visual analogue scale (EQ-VAS) score (F = 75.217, P < 0.001) and the highest K10 score (F = 61.90, P < 0.001). Moreover, increased age (OR = 0.819, 95% CI: 0.817–0.973), never having drunk alcohol (OR = 0.107, 95% CI: 0.023, 0.488), and increased EQ-VAS scores (OR = 0.935, 95% CI: 0.919, 0.952) were protective factors for quality of life, while working in obstetrics and gynaecology (OR = 6.457, 95% CI:1.852, 22.512) and higher K10 scores (OR = 1.153, 95% CI: 1.100, 1.209) were risk factors for quality of life. Conclusion The results indicated significant heterogeneity in the types of QoL and identified predictors of QoL. These findings provide basic information for the development of nursing interventions to improve quality of life and identified specific characteristics that should be considered during intervention development.